The increasing number of cloud computing infrastructure and the users’ demands for services has made the cloud resource management an impossible task to be manually performed by human operators. In this paper, we surveyed the state of the art of cloud resource management for infrastructure as a service. We provided an overview of the recent research findings and technologies while focusing on the resource management techniques such as resource provisioning, resource discovery, resource monitoring, resource mapping, resource allocation, resource consolidation, resource modeling, resource scheduling. This survey triggered innovative methods to handle the existing problems of resource management in cloud computing and we hope that it can be used as a source of interested readers to understand the existing methodologies in this research area for future enhancements.

Power substations play a crucial role in ensuring the reliable transmission of electricity to residential and commercial establishments. This paper addresses the critical issue of insulator fault detection in electric substations, emphasizing the importance of timely identification to prevent accidents. Conventional inspection methods relying on manual intervention pose high risk, especially in high-voltage substations. To overcome this, infra-red (IR) thermal cameras are employed, but human analysis of IR thermal images is subjective and time-consuming. The current paper develops a computer vision system uses advanced segmentation algorithms to automatically detect and categorize defects in insulators based on Infrared Thermography (IRT) images. The proposed system combines unsupervised clustering for efficient training with supervised learning for accurate classification. In particular, it first employs clustering techniques for unsupervised learning to reduce human labeling needs. Then, in the supervised learning phase, a Gaussian kernel support vector machine (SVM) algorithm classifies various insulator defects using extracted features. The trained algorithm utilizes the extracted characteristics to effectively identify and classify several fault types in thermal images of insulators. The system’s efficiency in quickly identifying faults enables timely intervention, preventing potential power outages and safety hazards.

Crop damage attributed to pest birds is an important problem, particularly in low-income countries. This paper describes a prototype system for pest bird detection using a Conv1D neural network model followed by scaring actions to reduce the presence of pest birds on farms. Acoustic recorders were deployed on farms for data collection, supplemented by acoustic libraries. The sounds of pest bird species were identified and labeled. The labeled data were used in Edge Impulse to train a tinyML Conv1D model to detect birds of interest. The model was deployed on Arduino Nano 33 BLE Sense (nodes) and XIAO (Base station) microcontrollers to detect the pest birds, and based on the detection, scaring sounds were played to deter the birds. The model achieved an accuracy of 96.1% during training and 92.99% during testing. The testing F1 score was 0.94, and the ROC score was 0.99, signifying a good discriminatory ability of the model. The prototype was able to make inferences in 53 ms using only 14.8 k of peak RAM and only 43.8 K of flash memory to store the model. Results from the prototype deployment in the field demonstrated successful detection and triggering actions and SMS messaging notifications. Further development of this novel integrated and sustainable solution will add another tool for dealing with pest birds.

In CP-ABE systems [1], attributes describe user's credentials and policies describe who can decrypt data from the server. Many researches have been conducted in Personal Health Records (PHR) to facilitate the mediation and direct connection of patient to different users (physicians, family members and clinic agents) through network. Ciphertext Policy Attribute-based Encryption mechanism is used to determine who can access sensitive PHR documents. In this paper, CP-ABE with Constant-size Ciphertext and Constant Computation-Cost is used to describe users' credentials, and a patient associate encrypted data with credentials to determine who can decrypt sensitive PHR from the server. Due to the large number involved in the access policy scheme this work is based on Constant-size Ciphertext and Constant Computation-cost.

Maternal and child deaths due to pregnancy-related complications are still a huge concern and more prevalent in developing countries. Early detection of complications can help avoid many of the reported cases. Due to long distances to nearby health facilities, lack of knowledge, and poverty, the majority of expectant mothers in rural developing nations are still adamant about prioritizing their health during this very sensitive period. This study aimed at designing and prototyping a system intended to help improve maternal health in rural areas of developing nations. This can be achieved by providing a relatively easy way to monitor the health of expectant mothers remotely and provide vital information in the form of periodic tips with automatic alerts and tracking in case of missed clinical appointments. Therefore, an architecture for context awareness and an IoT-based system is proposed to improve pregnancy monitoring in Africa and thus reduce fatalities and complications. The system uses a pulse sensor, temperature sensor, and GPS module to collect data with an Arduino as the microcontroller. The collected data is sent to the cloud using the GSM module. The cloud data is stored, processed, analysed, and then sent to the health worker. In case of any health complication or an appointment is missed, the health officials are alerted in real-time. This system will ensure a timely response in an emergency, thus leading to better health and contributing to sustainable development goals.

Massive surge in mobile data traffic, high power consumption, and limited spectrum availability are the major challenges of existing cellular networks. Industries and academia have conducted many studies and proposed solutions to meet the exploding user demand and minimize the network operators’ capital and operational expenditures. The cloud radio access network (CRAN) system is a paradigm shift for cellular networks due to its central processing, which is also considered a potential candidate for 5G and beyond fifth generation (5G) networks. This paper presents a comprehensive overview of the state-of-the-art CRAN architecture and critically analyzes its performance metrics. Moreover, it provides the first holistic insight into the recent advances in CRAN architecture; integrating CRAN architecture with state-of-the-art technologies such as massive multiple-input multiple output (MIMO) systems by equipping the transmitter and the receiver with multiple antennas, intelligent reflecting surfaces, unmanned aerial vehicles, integrated sensing and communication, rate splitting method, and energy harvesting. Further, the related research challenges are also discussed, proposing future directions.